A new combination classification of pixel- and object-based methods

Abstract

This study presents a novel classification method using a combination of pixel- and object-based classifications, which includes the pixel-based uncertainty classification, object-based classification, and combined pixel- and object-based classification. As the spatial resolution increases, the complexity of land covers and the degree of information uncertainty in remote-sensing imagery increase, making remote-sensing image classification more difficult. For high-resolution image classification, using the pixel-based method it is easy to misclassify the different components with characteristic variations within the same land cover as different categories, and with the object-based method it is easy to misclassify the different categories of land cover with a strong spatial correlation as the same category. By using the proposed method, the pixel- and object-based classifications are performed on the image respectively, and the pixel-based classification result is utilized to correct the object-based classification result to obtain the optimized synthesis classification result. The experiments indicate that the combined classification method not only makes full use of the advantages of the individual-level methods, but also overcomes their disadvantages and produces higher classification accuracy than the single pixel- or object-based method. The accuracy improvement with the combined classification in the three experiments is 8.3, 9.5, and 13.2% relative to the pixel-based classification, and 7.2, 6.1, and 8.1% relative to the object-based classification.